A foreground marker based centroid initialized Geodesic active contours for histopathological image segmentation

Shivamurthy, P M; Nagabhushan, T. N.; Basavaraj, Vijaya

doi:10.1109/ccip.2016.7802859

Cited by 3 publications

(1 citation statement)

References 14 publications

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“… Contrast enhancement: The contrast of resized input image

I^{G}

is enhanced in this step. The particular process adjusts the image's intensity [34–36] so that the visibility of image highly gets improved by adopting the relative darkness as well as the brightness of

I^{G}

, which is specified in (1), where J refers to the contrast enhancement of the image. Hence, the present

I^{G}

converts into a new grey level image

I_{new}^{G}

J = ()(, \begin{matrix} ()(, {((I - l o w_i n) / (h i g h_i n - l o w_i n))}^{γ}) \\ * ()(, h i g h_o u t - l o w_o u t) \end{matrix}) + l o w_o u t

Grey thresholding: In this paper, the grey thresholding is processed using Otsu's thresholding [37], which integrates either one black or white pixel in correspondence with the respective position that based on grey intensity.…”

Section: Proposed Caries Detection Modelmentioning

confidence: 99%

Intelligent system with dragonfly optimisation for caries detection

Patil

Kulkarni

Bhise

2019

IET Image Processing

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“… Contrast enhancement: The contrast of resized input image

I^{G}

I^{G}

, which is specified in (1), where J refers to the contrast enhancement of the image. Hence, the present

I^{G}

converts into a new grey level image

I_{new}^{G}

J = ()(, \begin{matrix} ()(, {((I - l o w_i n) / (h i g h_i n - l o w_i n))}^{γ}) \\ * ()(, h i g h_o u t - l o w_o u t) \end{matrix}) + l o w_o u t

Section: Proposed Caries Detection Modelmentioning

confidence: 99%

Intelligent system with dragonfly optimisation for caries detection

Patil

Kulkarni

Bhise

2019

IET Image Processing

View full text Add to dashboard Cite

An active contour for segmentation of images of low contrast and blurred boundaries

Tan

2017

2017 International Conference on Computer, Information and Telecommunication Systems (CITS)

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A Combined Expectation Maximization and Marker Controlled Watershed Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images

Shivamurthy¹,

Nagabhushan²,

Basavaraj³

2018

IJCA

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Detection of significant biomarker has been a major issue in the pathological study of cancerous tissue. The microscopic observation of H&E stained histopathological slides involves the study of various tissue objects and their significant traits influencing the cancer grading. The change in shape and size of the nuclei objects (nuclear pleomorphism) contributes much significantly in grading the cancer. With the advent of various imaging studies over a digitized Histopathological image, Active contour based segmentation approaches are considered to be much potential scheme in detecting the nuclei within occlusions and extracting their irregular boundaries. In this research, a novel approach of driving the contours of distance regularized level sets using an improved watershed transformation, has been presented. An Expectation Maximization based morphologically precomputed shape prior is used to extract the foreground markers, which controls the watershed transformation. The result of watershed transformation is used compute the centroid of nuclei to serve the initialization of the contour and the proposed gradient to drive the same efficiently. The study performed over the Benign and Malignant tissue images from BreakHis dataset has shown the efficacy of the methodology in terms of object detection accuracy and overlap resolution. The segmentation accuracy is compared to that of Geodesic active contours, based on the ground truth generated by expert pathologist.

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A foreground marker based centroid initialized Geodesic active contours for histopathological image segmentation

Cited by 3 publications

References 14 publications

Intelligent system with dragonfly optimisation for caries detection

Intelligent system with dragonfly optimisation for caries detection

An active contour for segmentation of images of low contrast and blurred boundaries

A Combined Expectation Maximization and Marker Controlled Watershed Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images

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